Overview

Dataset statistics

Number of variables13
Number of observations75630
Missing cells0
Missing cells (%)0.0%
Duplicate rows2204
Duplicate rows (%)2.9%
Total size in memory7.5 MiB
Average record size in memory104.0 B

Variable types

NUM10
CAT3

Warnings

Dataset has 2204 (2.9%) duplicate rows Duplicates
Date has a high cardinality: 1096 distinct values High cardinality
Net_Cost is highly correlated with Basic_Cost and 5 other fieldsHigh correlation
Basic_Cost is highly correlated with Net_Cost and 5 other fieldsHigh correlation
Basic_Selling_Price is highly correlated with Basic_Cost and 5 other fieldsHigh correlation
Selling_Price is highly correlated with Basic_Cost and 5 other fieldsHigh correlation
Tax is highly correlated with Basic_Cost and 5 other fieldsHigh correlation
Cash _Received is highly correlated with Basic_Cost and 5 other fieldsHigh correlation
Profit is highly correlated with Basic_Cost and 5 other fieldsHigh correlation
Discount has 36306 (48.0%) zeros Zeros

Reproduction

Analysis started2022-04-03 00:04:36.277445
Analysis finished2022-04-03 00:05:37.705974
Duration1 minute and 1.43 second
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Item
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
Mid Night Deal
8325 
Zinger Combo
8325 
Bucket of Fries
6815 
Hot Wings
5311 
Xtreme Box
5290 
Other values (14)
41564 
ValueCountFrequency (%) 
Mid Night Deal832511.0%
 
Zinger Combo832511.0%
 
Bucket of Fries68159.0%
 
Hot Wings53117.0%
 
Xtreme Box52907.0%
 
Chicky Meal52807.0%
 
Nuggests45376.0%
 
chicken Piece37785.0%
 
Arabian Rice37765.0%
 
Pizza Twister30434.0%
 
Other values (9)2115028.0%
 
2022-04-03T05:35:37.892222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-03T05:35:38.091248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length12
Mean length11.92977654
Min length7

Date
Categorical

HIGH CARDINALITY

Distinct1096
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
20-Feb-12
 
135
20-Feb-11
 
135
20-Feb-13
 
135
18-Feb-11
 
104
18-Feb-13
 
104
Other values (1091)
75017 
ValueCountFrequency (%) 
20-Feb-121350.2%
 
20-Feb-111350.2%
 
20-Feb-131350.2%
 
18-Feb-111040.1%
 
18-Feb-131040.1%
 
18-Feb-121040.1%
 
23-Feb-121000.1%
 
23-Feb-111000.1%
 
23-Feb-131000.1%
 
31-Dec-11990.1%
 
Other values (1086)7451498.5%
 
2022-04-03T05:35:38.292218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-03T05:35:38.529789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length9
Min length9

Quantity
Real number (ℝ≥0)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.62968399
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Memory size590.9 KiB
2022-04-03T05:35:38.708901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q315
95-th percentile19
Maximum45
Range44
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.559944001
Coefficient of variation (CV)0.523058259
Kurtosis-1.0972715
Mean10.62968399
Median Absolute Deviation (MAD)5
Skewness-0.09990373168
Sum803923
Variance30.91297729
MonotocityNot monotonic
2022-04-03T05:35:39.223553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
1663198.4%
 
1256647.5%
 
652947.0%
 
1348896.5%
 
1541365.5%
 
1141245.5%
 
240955.4%
 
938565.1%
 
737855.0%
 
1837294.9%
 
Other values (18)2973939.3%
 
ValueCountFrequency (%) 
132834.3%
 
240955.4%
 
333664.5%
 
434314.5%
 
523483.1%
 
ValueCountFrequency (%) 
452< 0.1%
 
301< 0.1%
 
261< 0.1%
 
255< 0.1%
 
244< 0.1%
 

Unit_Cost
Real number (ℝ≥0)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.9152453
Minimum0
Maximum1600
Zeros10
Zeros (%)< 0.1%
Memory size590.9 KiB
2022-04-03T05:35:39.492187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile110
Q1250
median350
Q3510
95-th percentile1150
Maximum1600
Range1600
Interquartile range (IQR)260

Descriptive statistics

Standard deviation316.9864231
Coefficient of variation (CV)0.7238533631
Kurtosis3.251742571
Mean437.9152453
Median Absolute Deviation (MAD)120
Skewness1.848236067
Sum33119530
Variance100480.3924
MonotocityNot monotonic
2022-04-03T05:35:39.792335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
350780010.3%
 
36043225.7%
 
22036174.8%
 
32033604.4%
 
55033484.4%
 
53031164.1%
 
11030294.0%
 
9030174.0%
 
51028593.8%
 
38028403.8%
 
Other values (28)3832250.7%
 
ValueCountFrequency (%) 
010< 0.1%
 
9030174.0%
 
11030294.0%
 
12012931.7%
 
14012001.6%
 
ValueCountFrequency (%) 
16007731.0%
 
15507191.0%
 
15007651.0%
 
115018102.4%
 
110016792.2%
 

Basic_Cost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct464
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4631.44916
Minimum0
Maximum40500
Zeros10
Zeros (%)< 0.1%
Memory size590.9 KiB
2022-04-03T05:35:40.046697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile460
Q11650
median3520
Q35850
95-th percentile13800
Maximum40500
Range40500
Interquartile range (IQR)4200

Descriptive statistics

Standard deviation4578.533457
Coefficient of variation (CV)0.9885746984
Kurtosis8.392388159
Mean4631.44916
Median Absolute Deviation (MAD)2080
Skewness2.544365951
Sum350276500
Variance20962968.61
MonotocityNot monotonic
2022-04-03T05:35:40.274472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
180010471.4%
 
42009561.3%
 
33009201.2%
 
56008491.1%
 
63008251.1%
 
48007541.0%
 
21007491.0%
 
14407451.0%
 
57606490.9%
 
39606450.9%
 
Other values (454)6749189.2%
 
ValueCountFrequency (%) 
010< 0.1%
 
902110.3%
 
1101290.2%
 
120560.1%
 
140870.1%
 
ValueCountFrequency (%) 
405001< 0.1%
 
320001000.1%
 
3100032< 0.1%
 
304005< 0.1%
 
300001130.1%
 

Delivery_Cost
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
100
44779 
120
30851 
ValueCountFrequency (%) 
1004477959.2%
 
1203085140.8%
 
2022-04-03T05:35:40.550166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-03T05:35:40.707817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:40.824528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Net_Cost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct751
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4739.607563
Minimum100
Maximum40600
Zeros0
Zeros (%)0.0%
Memory size590.9 KiB
2022-04-03T05:35:41.107814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile570
Q11750
median3640
Q35950
95-th percentile13920
Maximum40600
Range40500
Interquartile range (IQR)4200

Descriptive statistics

Standard deviation4578.239994
Coefficient of variation (CV)0.9659533902
Kurtosis8.388573722
Mean4739.607563
Median Absolute Deviation (MAD)2060
Skewness2.543840906
Sum358456520
Variance20960281.44
MonotocityNot monotonic
2022-04-03T05:35:41.363700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19007311.0%
 
43006370.8%
 
34005220.7%
 
15405120.7%
 
12005050.7%
 
8205040.7%
 
22004440.6%
 
64004430.6%
 
7604400.6%
 
43204320.6%
 
Other values (741)7046093.2%
 
ValueCountFrequency (%) 
1006< 0.1%
 
1204< 0.1%
 
1901260.2%
 
2101530.2%
 
220530.1%
 
ValueCountFrequency (%) 
406001< 0.1%
 
3212010< 0.1%
 
32100900.1%
 
3112012< 0.1%
 
3110020< 0.1%
 

Basic_Selling_Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct751
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6161.489832
Minimum130
Maximum52780
Zeros0
Zeros (%)0.0%
Memory size590.9 KiB
2022-04-03T05:35:41.651155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile741
Q12275
median4732
Q37735
95-th percentile18096
Maximum52780
Range52650
Interquartile range (IQR)5460

Descriptive statistics

Standard deviation5951.711992
Coefficient of variation (CV)0.9659533902
Kurtosis8.388573722
Mean6161.489832
Median Absolute Deviation (MAD)2678
Skewness2.543840906
Sum465993476
Variance35422875.64
MonotocityNot monotonic
2022-04-03T05:35:41.951130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
24707311.0%
 
55906370.8%
 
44205220.7%
 
20025120.7%
 
15605050.7%
 
10665040.7%
 
28604440.6%
 
83204430.6%
 
9884400.6%
 
56164320.6%
 
Other values (741)7046093.2%
 
ValueCountFrequency (%) 
1306< 0.1%
 
1564< 0.1%
 
2471260.2%
 
2731530.2%
 
286530.1%
 
ValueCountFrequency (%) 
527801< 0.1%
 
4175610< 0.1%
 
41730900.1%
 
4045612< 0.1%
 
4043020< 0.1%
 

Discount
Real number (ℝ≥0)

ZEROS

Distinct702
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.8677284
Minimum0
Maximum2639
Zeros36306
Zeros (%)48.0%
Memory size590.9 KiB
2022-04-03T05:35:42.124437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.85
Q3256.1
95-th percentile661.05
Maximum2639
Range2639
Interquartile range (IQR)256.1

Descriptive statistics

Standard deviation281.9941415
Coefficient of variation (CV)1.689926172
Kurtosis12.13126442
Mean166.8677284
Median Absolute Deviation (MAD)31.85
Skewness3.052492163
Sum12620206.3
Variance79520.69583
MonotocityNot monotonic
2022-04-03T05:35:42.351074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03630648.0%
 
123.54620.6%
 
279.53580.5%
 
318.52980.4%
 
310.72870.4%
 
193.72870.4%
 
1432850.4%
 
370.52780.4%
 
120.92720.4%
 
235.32610.3%
 
Other values (692)3653648.3%
 
ValueCountFrequency (%) 
03630648.0%
 
6.54< 0.1%
 
7.81< 0.1%
 
12.351240.2%
 
13.651360.2%
 
ValueCountFrequency (%) 
26391< 0.1%
 
2086.5900.1%
 
2022.81< 0.1%
 
2021.511< 0.1%
 
1982.54< 0.1%
 

Selling_Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5994.622104
Minimum123.5
Maximum50141
Zeros0
Zeros (%)0.0%
Memory size590.9 KiB
2022-04-03T05:35:42.580997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum123.5
5-th percentile728
Q12262
median4680
Q37582.9
95-th percentile17784
Maximum50141
Range50017.5
Interquartile range (IQR)5320.9

Descriptive statistics

Standard deviation5767.503362
Coefficient of variation (CV)0.9621129176
Kurtosis8.180287113
Mean5994.622104
Median Absolute Deviation (MAD)2600
Skewness2.515167581
Sum453373269.7
Variance33264095.03
MonotocityNot monotonic
2022-04-03T05:35:42.891050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2346.54620.6%
 
44464120.5%
 
5310.53580.5%
 
9883180.4%
 
15603140.4%
 
6051.52980.4%
 
51612910.4%
 
20022900.4%
 
3680.32870.4%
 
5903.32870.4%
 
Other values (1352)7231395.6%
 
ValueCountFrequency (%) 
123.54< 0.1%
 
1302< 0.1%
 
148.21< 0.1%
 
1563< 0.1%
 
234.651240.2%
 
ValueCountFrequency (%) 
501411< 0.1%
 
4175610< 0.1%
 
4045611< 0.1%
 
404309< 0.1%
 
396761< 0.1%
 

Tax
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1019.085758
Minimum20.995
Maximum8523.97
Zeros0
Zeros (%)0.0%
Memory size590.9 KiB
2022-04-03T05:35:43.225360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.995
5-th percentile123.76
Q1384.54
median795.6
Q31289.093
95-th percentile3023.28
Maximum8523.97
Range8502.975
Interquartile range (IQR)904.553

Descriptive statistics

Standard deviation980.4755716
Coefficient of variation (CV)0.9621129176
Kurtosis8.180287113
Mean1019.085758
Median Absolute Deviation (MAD)442
Skewness2.515167581
Sum77073455.85
Variance961332.3465
MonotocityNot monotonic
2022-04-03T05:35:43.475309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
398.9054620.6%
 
755.824120.5%
 
902.7853580.5%
 
167.963180.4%
 
265.23140.4%
 
1028.7552980.4%
 
877.372910.4%
 
340.342900.4%
 
625.6512870.4%
 
1003.5612870.4%
 
Other values (1352)7231395.6%
 
ValueCountFrequency (%) 
20.9954< 0.1%
 
22.12< 0.1%
 
25.1941< 0.1%
 
26.523< 0.1%
 
39.89051240.2%
 
ValueCountFrequency (%) 
8523.971< 0.1%
 
7098.5210< 0.1%
 
6877.5211< 0.1%
 
6873.19< 0.1%
 
6744.921< 0.1%
 

Cash _Received
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7013.707861
Minimum144.495
Maximum58664.97
Zeros0
Zeros (%)0.0%
Memory size590.9 KiB
2022-04-03T05:35:43.775342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum144.495
5-th percentile851.76
Q12646.54
median5475.6
Q38871.993
95-th percentile20807.28
Maximum58664.97
Range58520.475
Interquartile range (IQR)6225.453

Descriptive statistics

Standard deviation6747.978934
Coefficient of variation (CV)0.9621129176
Kurtosis8.180287113
Mean7013.707861
Median Absolute Deviation (MAD)3042
Skewness2.515167581
Sum530446725.5
Variance45535219.69
MonotocityNot monotonic
2022-04-03T05:35:44.024314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2745.4054620.6%
 
5201.824120.5%
 
6213.2853580.5%
 
1155.963180.4%
 
1825.23140.4%
 
7080.2552980.4%
 
6038.372910.4%
 
2342.342900.4%
 
4305.9512870.4%
 
6906.8612870.4%
 
Other values (1352)7231395.6%
 
ValueCountFrequency (%) 
144.4954< 0.1%
 
152.12< 0.1%
 
173.3941< 0.1%
 
182.523< 0.1%
 
274.54051240.2%
 
ValueCountFrequency (%) 
58664.971< 0.1%
 
48854.5210< 0.1%
 
47333.5211< 0.1%
 
47303.19< 0.1%
 
46420.921< 0.1%
 

Profit
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1376
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1255.014541
Minimum23.5
Maximum9636
Zeros0
Zeros (%)0.0%
Memory size590.9 KiB
2022-04-03T05:35:44.307749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile152.75
Q1462
median982.3
Q31586.25
95-th percentile3654.24
Maximum9636
Range9612.5
Interquartile range (IQR)1124.25

Descriptive statistics

Standard deviation1203.916507
Coefficient of variation (CV)0.9592849075
Kurtosis8.032400532
Mean1255.014541
Median Absolute Deviation (MAD)542.85
Skewness2.472160395
Sum94916749.7
Variance1449414.957
MonotocityNot monotonic
2022-04-03T05:35:44.528563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
446.54620.6%
 
1010.53580.5%
 
3603140.4%
 
1151.52980.4%
 
11912910.4%
 
4622900.4%
 
700.32870.4%
 
1123.32870.4%
 
5172850.4%
 
12902790.4%
 
Other values (1366)7247995.8%
 
ValueCountFrequency (%) 
23.54< 0.1%
 
28.21< 0.1%
 
302< 0.1%
 
363< 0.1%
 
44.651240.2%
 
ValueCountFrequency (%) 
963610< 0.1%
 
95411< 0.1%
 
933611< 0.1%
 
93309< 0.1%
 
91561< 0.1%
 

Interactions

2022-04-03T05:35:04.791642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:05.076567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:05.360430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:05.545798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:05.793761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:06.092747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:06.276170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:06.459282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:06.708421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:06.991987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:07.209416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:07.492705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:07.809361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:08.043676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:08.393603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:08.764050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:09.222960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:09.698886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:10.065555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:10.490356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:11.093416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:11.455987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:11.848555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:12.237867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:12.658003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:12.998904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:13.353046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:13.726311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:14.115601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:14.518480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:14.910834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:15.289417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:15.693656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:16.114026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:16.537544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:16.948059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:17.319109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:17.716593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:18.078092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:18.492848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:18.861374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:19.217661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:19.587404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:19.975401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:20.359014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:20.714422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:21.082961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:21.437139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:21.769491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:22.116804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:22.455632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:22.763130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:23.094517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:23.418972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:23.764604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:24.076855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:24.424991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:24.770450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:25.081066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:25.291796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:25.709470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:25.966808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:26.259438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:26.529526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:26.849695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:27.171049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:27.497031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:27.746307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:28.088001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:28.480720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:28.783694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:29.091605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:29.408212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:29.692555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:29.959643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:30.145203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:30.327326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:30.529968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:30.726395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:30.925957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:31.124811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:31.458159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:31.759223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:32.075865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:32.274835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:32.524937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:32.844133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:33.105024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:33.391460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:33.591471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:33.875770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:34.075802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:34.343054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:34.608144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:34.843555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:35.027398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:35.258188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:35.512511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:35.808213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:36.008004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-03T05:35:44.809986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-03T05:35:45.123266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-03T05:35:45.490921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-03T05:35:45.825221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-04-03T05:35:46.140935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-04-03T05:35:36.530380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T05:35:37.174676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

ItemDateQuantityUnit_CostBasic_CostDelivery_CostNet_CostBasic_Selling_PriceDiscountSelling_PriceTaxCash _ReceivedProfit
0Soft Drink01-Jan-1117110187012019902587129.352457.65417.80052875.4505467.65
1Hot Wings01-Jan-11339011701201290167783.851593.15270.83551863.9855303.15
2Pizza Twister01-Jan-1183502800100290037700.003770.00640.90004410.9000870.00
3Hot Wings01-Jan-11239078012090011700.001170.00198.90001368.9000270.00
4Xtreme Box01-Jan-11410504200100430055900.005590.00950.30006540.30001290.00
5Mid Night Deal01-Jan-1117320544010055407202360.106841.901163.12308005.02301301.90
6Hot Wings01-Jan-11139039010049063731.85605.15102.8755708.0255115.15
7Mid Night Deal01-Jan-11103203200100330042900.004290.00729.30005019.3000990.00
8Bucket of Fries01-Jan-117220154012016602158107.902050.10348.51702398.6170390.10
9Nuggests01-Jan-1193002700100280036400.003640.00618.80004258.8000840.00

Last rows

ItemDateQuantityUnit_CostBasic_CostDelivery_CostNet_CostBasic_Selling_PriceDiscountSelling_PriceTaxCash _ReceivedProfit
75620Mineral Water31-Dec-13149012601201380179489.701704.30289.73101994.0310324.30
75621Zinger Combo31-Dec-1313510715010072509425471.258953.751522.137510475.88751703.75
75622Hot Wings31-Dec-1363902580100268034840.003484.00592.28004076.2800804.00
75623Super Spicy Burger31-Dec-1318310630010064008320416.007904.001343.68009247.68001504.00
75624Zinger Combo31-Dec-136510330010034004420221.004199.00713.83004912.8300799.00
75625Mineral Water31-Dec-131190990120111014430.001443.00245.31001688.3100333.00
75626Bucket of Fries31-Dec-13122025010035045522.75432.2573.4825505.732582.25
75627Chicky Meal31-Dec-13113504180100428055640.005564.00945.88006509.88001284.00
75628Pizza Twister31-Dec-13203507600120772010036501.809534.201620.814011155.01401814.20
75629Bucket of Fries31-Dec-13182204500100460059800.005980.001016.60006996.60001380.00

Duplicate rows

Most frequent

ItemDateQuantityUnit_CostBasic_CostDelivery_CostNet_CostBasic_Selling_PriceDiscountSelling_PriceTaxCash _ReceivedProfitcount
160Bucket of Fries20-Feb-136250150010016002080104.001976.00335.92002311.9200376.003
171Bucket of Fries22-Jul-1222304601205807540.00754.00128.1800882.1800174.003
193Bucket of Fries25-Apr-1320250500010051006630331.506298.501070.74507369.24501198.503
612Hot Wings06-Apr-13164306880100698090740.009074.001542.580010616.58002094.003
733Mid Night Deal01-Feb-1215350525010053506955347.756607.251123.23257730.48251257.253
742Mid Night Deal01-Sep-1312360432010044205746287.305458.70927.97906386.67901038.703
845Mid Night Deal08-Jul-1220350700010071009230461.508768.501490.645010259.14501668.503
866Mid Night Deal10-Aug-1218350630010064008320416.007904.001343.68009247.68001504.003
988Mid Night Deal19-Aug-1218350630010064008320416.007904.001343.68009247.68001504.003
1100Mid Night Deal27-Jun-1217350595010060507865393.257471.751270.19758741.94751421.753